Abstract:The reform of transmission and distribution (T&D) pricing is a crucial task in achieving the "control the middle, unleash both ends" electricity system reform. A scientifically and reasonably formed transmission and distribution pricing mechanism is of significant importance in creating a fair and orderly electricity market environment and promoting the optimal allocation of electricity resources. In this regard, a transmission pricing method that takes into account the marginal value of lines and spatial?temporal information of users is proposed. Firstly, the cost of the transmission lines is decomposed into two parts: expansion cost reflecting the marginal value of the lines and residual cost, both of which are recovered. Secondly, the expansion cost is devoid of subjective assumptions and is objectively derived based on economic principles. It is determined through the computation of congestion surplus revenue in the electricity spot market clearing under the marginal electricity pricing mechanism at nodes. Next, the residual cost is allocated according to the usage of the transmission lines at each node using the distribution factor method and postage stamp method. Finally, the proposed transmission pricing method is validated for effectiveness using both a 3?node system and the IEEE 30?node system.

Abstract:Under the background of constructing a unified national electricity market, the integration between provincial markets has been deepening. When organizing inter?provincial spot trading, it is necessary to accurately delineate the regional equivalent values of the node network, taking into account each province's information security and limited computing power. These equivalent values are then embedded into the clearance model of inter?provincial spot trading.use congestion power transfer weights of internal nodes towards the price zone have time?varying characteristics and are The transfer distribution factor that can meet the requirement of computing efficiency. In this paper, we first crucial for the accuracy of the clearance calculation. Traditional methods often use a daily fixed mean value as an approximation, which is no longer sufficient to meet the requirements. The increasing proportion of renewable energy exacerbates the uncertainty of system operation, manifested in frequent changes in power flow transfer and an increase in volatile congestion scenarios, which will further affect the effectiveness of traditional fixed regional equivalent methods. This paper proposes a price zone division and approximation calculation method adapted to a high proportion of renewable energy. Firstly, based on consensus clustering, typical congestion scenarios in the electricity market are constructed, and a price zone equivalent partition method is proposed. Secondly, an improved calculation method for power transfer weights of internal nodes towards the price zone is presented. The problem of reduced calculation accuracy due to time?varying power injections is avoided by classifying node characteristics and calculating the power transfer weight matrix accordingly. Finally, the effectiveness of the proposed model is verified through validation using the IEEE 118?node case.

Abstract:Considering factors such as the dynamic, systematic, and long?term nature of the electricity market, there are often many counter?intuitive issues, the correct inferences contradict people's intuitions. In this regard, taking the zonal pricing mechanism as an example, counterintuitive issues within the electricity pricing mechanism are explored. Firstly, based on mechanism design theory, Firstly, based on mechanism design theory, the incentive compatibility issues in mechanism design and their relationship with counter?intuitive phenomena are analyzed. In response to the dynamic characteristics of market participants, an analytical approach that combines static and dynamic analyses is presented for addressing counterintuitive issues. Finally, using the generation?side nodal weighted average price as the settlement price as the example, a simulation analysis on potential counter?intuitive issues arising from the mentioned mechanism is carried out from both the static and dynamic perspectives. The results indicate that under this mechanism, due to some market participants declaring prices based on individual rationality, it does not necessarily lead to a reduction in electricity procurement costs. To address this, there is a need to design corresponding cost compensation and sharing mechanisms for such units, ensuring compliance with incentive compatibility constraints in the market.

Abstract:The scope of China's electricity market is primarily at the provincial level, and currently, inter?provincial electricity trading plays a role in balancing surplus and deficit. This province?centric market structure results in an insufficient and incomplete optimization process for electricity resource allocation, leading to the coexistence of underutilized low?cost power plants and operational high?cost ones within the regional scope. Firstly, with the goal of minimizing the overall power supply cost, all market participants within the region are centrally cleared. Secondly, a measurement model for inter?provincial barriers is constructed after the clearance based on the minimum total cost. Subsequently, utilizing the Pareto optimization principle, compensation is provided to affected entities to eliminate barriers. Finally, the mechanism includes the sharing of net market revenue. This approach not only facilitates power substitution but also ensures that all market participants are not adversely affected, thereby eliminating inter?provincial barriers' impact on resource optimization and promoting the rational distribution of electricity resources and cost reduction within the region.

Abstract:Under the background of “double carbon”, the participation of industrial users with regulatory potential in demand?side management can improve the flexibility of the power system, relieve the pressure of power supply, and ensure the safe and stable operation of the power system. However, the traditional power management method of industrial users is generally 0?1 mode, which seriously affects the continuity of production, cannot guarantee the maximization of social benefits, and cannot become a normalized demand?side management mechanism to support the goal of ‘double carbon’. Therefore, this paper proposes a refined demand response decision?making method considering the production characteristics of industrial users. Firstly, the four major characteristics of enterprises power consumption, namely, guaranteed power supply, periodicity, continuity and coupling are analyzed. Then, with the goal of maximizing the power consumption of all users, the power consumption optimization model considering the internal constraints of industrial users, such as the minimum guaranteed power supply, the minimum power consumption cycle, the maximum load change rate and the load coupling correlation, as well as the external constraints such as the total power consumption and the load curve is constructed. On this basis, in order to ensure the reliability of model solving and improve the efficiency of model solving, a linearized solution method is proposed. Finally, based on the actual data of an industrial park in an eastern province of our country, the proposed model is verified. The results show that the proposed method can guarantee the production characteristics of industrial users and improve the benefits of enterprises under the premise of meeting the control objectives of total power consumption and load curve.

Abstract:The internal leakage magnetic field of transformer is an important criterion for determining the early fault of transformer winding. In actual operation, noise can interfere with the detection of the leakage magnetic field, thereby affecting the judgment of the fault status. Therefore, firstly, genetic algorithms are used with sample entropy as the fitness function to optimize the parameters of variational mode decomposition (VMD). Subsequently, the relevant modes obtained from VMD are processed using wavelet thresholding to eliminate residual noise. Next, feature vectors are selected and extracted from the denoised leakage magnetic field signals. These feature vectors are then input into an improved extreme learning machine (ELM) for training and classification, achieving early fault diagnosis of transformer windings. The results of simulation and dynamic experiment show that this method exhibits a good denoising performance, effectively restoring the original leakage magnetic field signal. Ultimately, it enables accurate identification of early faults in transformer windings.

Abstract:Modular multilevel converters have the problem of three?phase current asymmetry and fluctuations in active and reactive power when the grid voltages are unbalanced. For this reason, the mathematical model for voltage imbalance in power grid under different control objectives is established, and a frequency reduction model predictive control strategy suitable for grid voltage imbalance is proposed, which realizes accurate tracking of positive and negative sequence currents, and then, this paper introduces frequency reduction factors to effectively reduce the switching frequency and reduce the switching loss of modular multilevel converters. An improved moving average algorithm is proposed when calculating the switching frequency, which effectively reduces the memory and calculation amount occupied by the switching frequency calculation. The simulation results verify the effectiveness of the proposed control strategy by building a simulation model in Matlab/Simulink.

Abstract:Aiming at overlap and fuzziness between fault boundaries, faults, and characteristics under load disturbances and measurement noise influence when the inverter is in an open?circuit state,, an inverter open circuit fault diagnosis model built upon the fast Fourier transform (FFT) and improved Takagi?Sugeno (T?S) fuzzy neural network (FNN) integration model is proposed based on the analysis of the characteristics of the inverter power tube open circuit fault. Firstly, fault characteristics are extracted when different types of open?circuit faults occur in the power tubes according to the three?phase output current waveforms of the inverter analyzed by the FFT. Secondly, the membership function layer of the antecedent network of the T?S fuzzy neural network is designed by using the rule self?splitting technology and fuzzy C?means, and the parameters of the T?S network are trained by leveraging the adaptive Levenberg?Marquardt algorithm. The trained T?S network is used to realize the diagnosis of multiple fault types and positions of the inverter power tubes. The example results show that the diagnostic accuracy of the proposed model is up to 96%, which can significantly improve the problems existing in the open?circuit fault diagnosis of inverter power tubes.

Abstract:The wave head is difficult to identify because of the complexity of traveling wave signals in distribution networks. Therefore, this paper proposes a fault location method for distribution networks based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and novel Teager energy operator (NTEO). Firstly, the CEEMDAN is used to denoise and decompose initial traveling wave signals, and fault characteristics of high?frequency components are enhanced by NTEO. Then, the initial traveling wave head reach time is accurately calibrated according to the instantaneous energy peak, thus the rapid and accurate fault location can be realized. Finally, Pscad/Matlab simulation results show that the proposed method can accurately locate faults with good adaptability to different fault types and transition resistances, and the ranging error can be controlled within 1%. Besides, compared with the traditional EMD method, it has higher location accuracy and faster calculation speed.

Abstract:In order to solve the problems of insufficient flexible resources and poor regulation characteristics of power system frequency regulation, a load frequency coordination optimization control method based on distributed model predictive control is proposed for the interconnected power system with the wide?area access of pumped storage and battery energy storage. Firstly, according to the frequency response characteristics of the main components in the interconnected power system, the state space model of the frequency response of the interconnected power system is established. Then, taking into account the constraints on the system, the optimal control targets are determined, and the controllers are designed in each area, respectively. Finally, the feasibility and effectiveness of the proposed load frequency coordinated optimization control method are verified in simulation. The results show that the method not only improves the frequency response characteristics of the wide?area hybrid energy storage interconnected power system, but also enables different energy storage forms to respond reasonably when the system frequency changes.

Abstract:To deal with the stable operation of multiple energy storage power stations participating in primary frequency regulation, a cooperative frequency regulation control strategy for energy storage power stations was proposed with the consideration of the State of Charge (SOC) equilibrium demand. Firstly, the frequency regulation control model of regional power grid with multiple energy storage power stations is established, and the basic control principle of energy storage power station participating in primary frequency regulation is analyzed. Secondly, based on the net power variation within the action zone and dead zone of frequency regulation, different frequency band power and coupling complementary characteristics of SOC balance power are analyzed. The feasibility of the energy storage SOC balanced control and coordinated control of frequency regulation demand is demonstrated, and frequency regulation is obtained on the basis of the design on the coordination of energy storage frequency regulation control strategy. The design method of key control parameters is given. The proposed method not only realizes the frequency regulation, but also reduces the SOC out-of-limit risk and enhances the frequency support effect. Finally, a typical regional power grid model is built and simulated with different frequency fluctuation conditions. The results show that the proposed control strategy can effectively improve the frequency quality and achieve the balance of the charged state of multiple energy storage stations without increasing the frequency regulation burden of the system.

Abstract:In order to effectively monitor the insulation condition of metal oxide arresters, a leakage current denoising method based on improved empirical mode decomposition is proposed. Firstly, the end point is extended to suppress the end effect by considering both waveform and amplitude similarity comprehensively, and then the leakage current signal without noise is reconstructed according to the comprehensive index that incorporates the smoothness and correlation of the intrinsic mode function. Subsequently, the weight parameters were dynamically adjusted and updated through the coordinate descent method to ensure the rationality and accuracy of the reconstructed signals. Finally, through analysis of simulated and measured data, it is confirmed that the proposed method can effectively eliminate the noise interference in the leakage current signal, and identify abnormal monitoring values, meeting the practical requirements of engineering.

Abstract:Aiming at the problem of the diversity of fault causes and the random uncertainty caused by the increasingly complex distribution network structure, a random fuzzy?based distribution network failure risk assessment method is proposed. Compared with the traditional risk assessment of power outages in distribution networks, this method describes the cumulative probability of failures in a certain area from a data?driven dimension through random fuzzification of parameter models. In response to the increasing power supply reliability expectations of power users, the entropy weight method is used to comprehensively consider factors such as the fault level of the power outage accident, the length of the power outage, the number of sensitive users and the number of affected users, and conduct risk assessment of the distribution network failure. The results show that the probability distribution of the number of failures calculated by using the random fuzzy theory can realize a more scientific and comprehensive analysis of the distribution network failures. The weight value of the risk assessment index determined by the entropy weight method can provide support for formulating reasonable and effective fault repair strategies, formulate response plans for sensitive users and increase the degree of power user ownership.

Abstract:Mountain fires pose a threat to the safe operation of transmission lines. Firstly, the historical pattern of mountain fire incidents in Hunan province from 2018 to 2020 is reviewed. On this basis, a database of mountain fire incidents is constructed. One?hot encoding technique is introduced to numerically process textual features such as the vegetation type along the transmission lines. Then, XGBoost technology is utilized to build a mountain fire risk assessment model for transmission lines. Aiming at imbalanced samples between forest fire incidents and normal operations, based on a cost?sensitive mechanism, a weighted objective function is presented to mitigate the problem of overlooking mountain fires caused by sample imbalances. Finally, the proposed mountain fire risk assessment model for transmission lines is tested using the forest fire incidents in Yongzhou City, Hunan Province, from 2020 to 2021 to validate its effectiveness.

Abstract:Intentional islanding operation of distribution networks with multi?source is an important measure to ensure the power supply of critical loads and improve the safety and reliability of the distribution system when a large?scale grid fails under extreme conditions. Therefore, according to the distribution of different types of distributed power supply and important load, with the goal of minimum path weight and maximum recovery of important load, the optimization model of the distribution network island black start scheme at the early stage of major power failure is established to ensure the recovery of critical load in the shortest possible time. The advantages of fast convergence speed and high calculation accuracy of the BFGS trust region algorithm are fully used to propose a dynamic power flow analysis method for a self?organizing restoration process in intentional islanded distribution networks with multi?source. Then, the dynamic power flow's frequency and node voltage results are used to judge the feasibility of the black start scheme. The improved IEEE 33?node power distribution system is taken as a simulation example to verify the effectiveness and innovation of the proposed method.

Abstract:As one necessary equipment in the high?voltage power system, once the transformer fails, protection devices may refuse to operate and cause the malfunction of power grids. Traditional current transformer fault diagnosis and classification methods firstly extract features from the input operation data, and then use a specific classifier to diagnosis, which lacks adaptive update processing for dynamic input information. In order to further improve the accuracy of traditional recursive neural networks, the process efficiency of long short?term memory neural networks, this paper proposes a fault diagnosis method based on the LSTM model of batch normalization (BN). This method does not require feature extraction and classifier design steps, where the fault can be classified directly, and can also be updated adaptively. Compared with other fault diagnosis methods, this method has higher diagnostic accuracy and diagnostic performance, which validating its good application value in the field of current transformer fault diagnosis.

Abstract:For the direct?current (dc) microgrid system with constant power load, in order to suppress the dc bus voltage oscillation caused by constant power load disturbance, the fixed?time sliding mode control method is used to deal with the issue of the dc bus voltage control. Firstly, the mathematical model of a dc microgrid system with constant power loads and an energy storage unit is established. Secondly, based on the fixed?time stability theory, an integral sliding surface and a fixed?time sliding mode controller are constructed. By analyzing the accessibility of the system states, the value of upper bound on convergence time of the system is estimated. Then, through the stability analysis of the sliding mode, the controller gain is obtained with the combination of the linear matrix inequality method. Finally, a dc microgrid system with two constant power loads and one energy storage unit is taken as an example for simulation verification. It is shown that the fixed?time sliding?mode controller can effectively resist the negative influence of constant power load disturbance on the dc bus voltage.

Abstract:Large scale virtual energy storage is a large?scale energy storage system composed of multiple discrete energy storage devices through virtualization technology in the power grid, in order to achieve power balance regulation of the power grid. Because of the randomness, fluctuation and intermittence features of new energy power generation, it is difficult to control the prediction error of new energy power. In order to improve the local consumption level of new energy and reduce the prediction error of new energy power, an optimal scheduling method of large?scale virtual energy storage to suppress the prediction error of new energy power is proposed. By setting the time resolution of new energy power prediction, the new energy power stabilizing distribution characteristics of large?scale virtual energy storage are counted, the distribution characteristics of new energy power prediction error are determined, the confidence interval of new energy power prediction error is estimated, the new energy prediction power is included in the power generation plan according to certain confidence degree, the constraint conditions of large?scale virtual energy storage to stabilize new energy power prediction error are designed, the optimal scheduling model of new energy power prediction error is constructed, and the optimal solution of the model is solved by using particle swarm optimization algorithm. The experimental results show that the proposed method is more sensitive to large?scale virtual energy storage to stabilize the prediction error of new energy power, with less change in high?energy load regulation and lower cost, and has remarkable economy and effectiveness.

Abstract:The active distribution network operation situation prediction is an important tool to guarantee the safety and stability of the distribution network and the hazard perception. For the fast and accurate prediction of active distribution network operation, this paper proposes an active distribution network short?term operation prediction method based on ICEEMDAN?TA?LSTM model. Firstly, the original sequence is decomposed into several stable time series components by improving the modal decomposition to reduce the irregularity of the original data. At the same time, the improved manta ray feeding optimization algorithm is used to optimize the model's hyperparameters to comprehensively improve the overall prediction accuracy of the model. Then, from the perspective of nodes and branches, the node voltage overrun margin, branch load severity and voltage/current fluctuation evaluation indexes are proposed to characterize the distribution network operation situation at multiple levels. Finally, the feasibility and effectiveness of the model proposed in this paper are verified by taking the improved IEEE 33 node as a typical calculation example.

Abstract:To fully explore the temporal and cyclical characteristics of electric power loads and further enhance the reliability and cost-effectiveness of energy storage planning for distribution networks, firstly, based on the similarity analysis of three typical load curves throughout the year, a more refined clustering is performed on various load curves using the ordered clustering method according to the annual time series. Subsequently, the minimum time unit for dynamically configuring mobile energy storage in the distribution network is planned on a monthly basis. Thereby an active distribution network energy storage planning method is proposed based on ordered clustering of loads. Finally, the proposed method is validated using the IEEE?33 node system. The case study results indicate that the energy storage configuration scheme, taking into account the actual temporal characteristics of the load, exhibits better economic efficiency and provides a more realistic reflection of the actual operation of the distribution network. The approach represents an effective extension of the flexible utilization of energy storage devices in distribution networks. It can better serve the dynamic operational conditions of active distribution networks, thereby enhancing the role of energy storage.

Abstract:The current method used for the expansion planning of the distribution network in a regional integrated energy system overlooks the analysis of the structure and operational characteristics of various systems within the integrated energy system. This oversight results in high pollutant emissions, electricity consumption, and gas consumption in the planned distribution network. Aiming at the regional integrated energy system, a planning method for distribution network expansion is proposed. Firstly, the structure of the regional integrated energy system is analyzed while models for heating, cooling, and power supply are constructed. Next, a two?tier optimization model is established where the upper?level model aims to minimize the overall planning cost, and the lower?level model aims to maximize energy utilization efficiency. The upper and lower?tier models can respectively extend the planning of the system distribution network and optimize the regional integrated energy system. The simulation results of a distribution network connected to a regional integrated energy system show that the proposed method results in a reduction of distribution network loss rate to 4%~5%, the load balancing rate reaching more than 90%, and significantly controlled consumption of electricity, gas, pollutant discharge and load power after planning

Abstract:The dynamic capacity increasing technique exploits the potential power transmission capacity of overhead conductors by assessing ampacity in real?time. However, the existing dynamic capacity increasing models face challenges such as the requirement for a large number of sensors or difficulties in the installation and maintenance of sensors. To address these issues, a dynamic environmental measurement decoupling model is proposed. Firstly, the model establishes the associated equations between transient temperature data of aluminum spheres and environmental variables. Subsequently the solution accuracy of each environmental variable from the perspective of sensitivity is analyzed, and the solution methods of environmental variables with different sensitivities are given. Finally, experiments are carried out to validate the accuracy of the environmental measurement decoupling model. The experimental results show that the calculation error of the model is less than 4% compared with the calculation results of IEEE Std 738?2012.

Abstract:In response to the challenge of existing assessment indicators and methods struggling to distinguish harmonic curve differences in typical scenarios where trend characteristics vary significantly and CP95 values are close, rarely considering the time series trend characteristics and incapable of representing the differences scientifically and rationally in harmonic levels, a high?voltage harmonic characteristic assessment approach that takes into account the time series trend characteristics of typical scenarios is proposed. Firstly, based on the dual excitation theory, the temporal trend characteristics of the data sequence are extracted, and a comprehensive evaluation index system that encompasses traditional harmonic indicators and temporal trend characteristic indicators is established for high?voltage harmonics covering time series trend characteristics. Subsequently, considering the interrelation characteristics among the established indicators, a comprehensive assessment model is presented for high?voltage harmonics in typical scenarios based on the combination of sequential relationship analysis and the CRITIC method. Finally, case analysis validates the rationality and effectiveness of the proposed method. The case analysis indicates that the established time series trend characteristic indicators can effectively characterize the temporal development characteristics of data curves. For multiple harmonic curves with close CP95 values, they can effectively represent the differences in harmonic levels. Finally, the case analysis demonstrates that the established temporal trend characteristic indicators can effectively represent the temporal development characteristics of data curves. For multiple harmonic curves with close CP95 values, the indicators can efficiently characterize the differences in harmonic levels. The analysis results validate the rationality and effectiveness of the proposed method.

Abstract:Considering the uncertainty of renewable energy station output, a two?stage optimization model for AC/DC parallel transmission channel power distribution of a power system with a high proportion of renewable energy is established based on the distributionally robust optimization (DRO) method. The objective function of the first stage is to minimize the sum of active power loss costs of all transmission lines in the forecast scenario. The objective function of the second stage is to minimize the expected adjustment cost of directly dispatching generators and DC transmission lines power under the worst?case probability distribution (PD). When constructing the ambiguity set of PDs, Hellinger distance is used to measure the distance between real PD and reference PD, and the Markov chain is used to describe the time correlation of renewable energy station output. The column and constraint generation algorithm is used to solve the two?stage DRO model to obtain the day?ahead power transmission schedules of AC/DC parallel transmission channels. Finally, with the case study on an actual Hybrid AC/DC power grid and the computational results demonstrate the correctness and effectiveness of the proposed model and algorithm.

Abstract:Aiming at the low?carbon emission requirements and load demand characteristics of the integrated energy system, a comprehensive energy capacity allocation method considering the ladder carbon trading mechanism and hydrogen?blended natural gas is proposed. First of all, in order to alleviate the output volatility of wind and solar energy, a hydrogen energy system with hydrogen energy storage as the medium is established. Secondly, a hybrid hydrogen natural gas integrated energy system architecture comprising hydrogen energy storage and liquefied natural gas is created under the consideration of the substitution effect of hydrogen energy on natural gas. Then a ladder carbon trading mechanism is introduced to constrain the carbon emissions of the system. Finally, the capacity allocation is optimized with the annual total investment cost, including operating costs and annualized investment costs, as the objective function. The simulation results of the numerical example verify that the proposed model and strategy can reduce carbon emissions, total annual investment cost, and have important reference value for integreated energy allocation.

Abstract:Due to the influence of lighting and harsh weather, the traditional image processing methods have low recognition efficiency in visual navigation of inspection robots. This paper proposes a visual navigation method for power inspection robots based on image preprocessing and semantic segmentation. An image enhancement method based on the adaptive gamma correction method is proposed to solve the influence of strong light, weak light and uneven illumination on the image. Aiming to the exposure conditions, the LSTM prediction model is used to adaptively adjust the camera angle to eliminate the exposure and improve the good exposure of the image. The improved DenseNet is used to semantically segment the navigation path and extract the path target area, fitting the robot's forward route through the pixel value distribution of the target area and calculate the offset, which provides the key parameters of robots to adjust the driving posture. Template matching is used to determine the direction, location and bifurcation signs in the navigation path. Experimental results show that the algorithm could effectively solve the problem of low recognition accuracy caused by lighting and adverse weather, and improve the accuracy of navigation and positioning of inspection robots in complex environments.

Abstract:Reducing the impulse grounding resistance of transmission towers is an effective measure to improve the lightning surge withstand capability of transmission lines. However, current methods for reducing resistance generally face challenges in balancing technical and economic feasibility, as well as construction difficulty. Adding short conductors to the grounding device combines the advantages of resistance reduction and simplified construction. This paper explores the use of three?dimensional interleaved short conductors to reduce the impulse grounding resistance of transmission towers, and provides the parameter optimization approach for short conductors. Firstly, considering the influence of nonlinear ionization in the soil, the impulse grounding resistance of grounding device with short conductors is calculated by the CDEGS software. Results indicate that the short conductors with three?dimensional interleaved structure exhibit superior resistance reduction effects. Subsequently, taking the resistance reduction rate per unit length and minimum impulse grounding resistance as indicators, the length and spacing arrangements of three?dimensional interleaved short conductors are optimized. Finally, taking a soil resistivity of 1000 Ω? m as an example, the optimal length and spacing of the short conductors are determined to be 1.1 m and 2.8 m, respectively. The addition of the three?dimensional interleaved structure short conductors to the grounding device can reduce the impulse grounding resistance by 16.52%. This paper provides a method guidance for the design of transmission tower grounding device.

Abstract:Scientific and reasonable early warning and assessment of conductor icing can help to take accurate response measures to prevent the freezing disaster risk in time. This paper presents a multi factor grey prediction model GM (1,N) suit for small sample database. Compared with the traditional neural network model, the proposed model requires lower sample size of the modeling database and corresponds to higher modeling and calculation efficiency. The degree of conductor icing can be predicted in real time according to meteorological parameters, which can realize the risk warning of transmission line conductor icing disaster. Based on the case analysis of the proposed model, the icing degree is divided into five grades in the engineering application scenario. It is found that the average error of the multi factor prediction model based on GM (1,N) grey theory in ice thickness prediction is 8.1%, and the risk warning accuracy of transmission line icing disaster is as high as 94%. In addition, the probability of judging the high risk level as the lower one can be decreased by adding a certain safety margin value near the critical value of the ice thickness. In the ice area, The application of the ice thickness grey prediction model proposed in this paper can guide the anti?ice work of transmission lines in icing area.